MOESM2 of Oestrous cycle-dependent equine uterine immune response to induced infectious endometritis

Additional file 2. Statistical results for gene expression levels of all analysed genes. ANOVA, Bonferroni and interaction model results for the pathogen recognition receptors TLR2, TLR4, NLRC5, the chemokines CCL2, CXCL9, CXCL10, CXCL11, the tissue inhibitor of metallopeptidases (TIMP1) and the antimicrobial peptides lysozyme, lipocalin (LCN2), lactoferrin, uteroferrin, secretory leukoprotease inhibitor (SLPI), uterocalin P19, secreted phospholipase A2 (sPLA2) and equine β-defensin 1 (EBD1)

Additional file 1: Figure S1. of Deep sequencing of the uterine immune response to bacteria during the equine oestrous cycle

Flow chart outlining the study design, time points of E. coli inoculations and collection of uterine samples. Three horses were assigned to group 1 and two to group 2. (PDF 12 kb

Additional file 2: Figure S2. of Deep sequencing of the uterine immune response to bacteria during the equine oestrous cycle

Heatmap of all genes analysed for significant differential expression between the two cycle stages before and 3 h after inoculation with E. coli. Each column represents one sample, while each row represents the log2 counts per million (CPM) of one gene with normalised standard scores (Z-expression values; −2/ light green to +2/ dark green). The cluster trees on the top and left margins represent the hierarchical relation between samples and gene counts, respectively. While the first letter stands for the horse, the second stands for (o)estrus (E) or dioestrus (D), 0 h stands for samples taken before inoculation and 3 h for samples taken 3 h thereafter. Note that biological replicates cluster according to their treatment with the exception of ED_3h, which clusters with the dioestrous samples before inoculation. (PNG 985 kb

Additional file 2: of Pipeline for the identification and classification of ion channels in parasitic flatworms

Figure S1. The number of conserved domains common to >75 % of the sequences in each ion channel subfamily within the training dataset. The proportion of the sequences in the subfamilies that share the number of domains is given in the graph. The number of conserved domains is grouped according to the ion channel families. Figure S2. The range of transmembrane domains predicted in training dataset ion channel proteins. Range of transmembrane domains per subfamily, grouped according to each ion channel family. Figure S3. Receiver operating characteristic (ROC) curves for each probabilistic classification method. Figure S4. Probability values of each ion channel subfamilies computed during classification of sequences in the test dataset. Figure A shows the relation between the probability values and classifications made by SVM classifier. Figure B shows average probability values for individual ion channel subfamilies. The average values were grouped according to the ion channel families. Figure S5. Characteristics of putative ion channels identified and classified from the test dataset (human and C. elegans proteins). Panel A: Test sequences ordered by their SVM probability value, with their identification grouping presented on the second y-axis. Most of the sequences classified using high probability values were classified in Groups 1 and 2. Panel B: Confidence in test data ion channel classifications by group and classification category. (DOCX 1083 kb

Additional file 2: Figure S1. of The Haemonchus contortus kinome - a resource for fundamental molecular investigations and drug discovery

Transcription profiles for kinase genes in all key developmental stages (egg, L1, L2, L3, L4 and adult) and both sexes (L4 and adult) of Haemonchus contortus for eleven individual kinase groups. Figure S2. All clusters of transcription profiles for Haemonchus contortus kinase genes based on the Ward-clustering method (k = 15). (DOCX 222 kb

Additional file 2: Figure S1. of Use of a bioinformatic-assisted primer design strategy to establish a new nested PCR-based method for Cryptosporidium

Alignment of sequences of the variable D8 domain of the large subunit of nuclear ribosomal RNA gene (LSU) representing Cryptosporidium and closely related apicomplexans, alveolates and dinoflagellates. Oligonucleotide primers (LSU2040F, LSU3020R; LSU2065F and LSU2557R) designed specifically to regions flanking the variable D8 domain are indicated in green. Nucleotide differences from the majority consensus of the alignment are highlighted. Figure S2. Alignment of sequences of the variable D8 domain of the large subunit of nuclear ribosomal RNA gene (LSU) representing Cryptosporidium derived from 45 faecal DNA samples. Nucleotide differences from the majority consensus of the alignment are highlighted. Colpodella angusta was included as an outgroup. (PDF 222 kb

Additional file 1: Table S1. of Use of a bioinformatic-assisted primer design strategy to establish a new nested PCR-based method for Cryptosporidium

Relevant information pertaining to small subunit ribosomal RNA gene (SSU) sequences obtained from amplicons produced from selected faecal DNA samples using a nested PCR method ([2]; see Methods section). These DNA samples were employed to assess the analytical specificity of large subunit ribosomal RNA gene (LSU) nested PCR assay established in this study (see also Methods section) (cf. Fig. 1). Sequence similarities (90–100%) were calculated with reference to the closest matched sequence in the GenBank database using BLASTn. Table S2. Pairwise comparison of sequence difference (%) in the variable D8 domain of the large subunit of the nuclear ribosomal RNA gene (LSU) used for the construction of the phylogenetic tree. Table S3. Pairwise comparison of sequence difference (%) in the region of the small subunit of the nuclear ribosomal RNA gene (SSU) used for the construction of the phylogenetic tree. Table S4. Salient information pertaining to the sequences of the variable D8 domain of the nuclear large subunit ribosomal RNA gene (LSU) used for the construction of the phylogenetic tree (cf. Fig. 2a). Sequences produced in this study are shown in bold-type. Table S5. Salient information pertaining to the sequences from the small subunit of the nuclear ribosomal RNA gene (SSU) used for the construction of the phylogenetic tree (cf. Fig. 2b). Sequences produced in this study are shown in bold-type. (XLSX 78 kb

Additional file 1: Figure S1. of Reconstruction of the insulin-like signalling pathway of Haemonchus contortus

A schematic representation of functional domains and motifs as predicted by InterProScan of members of the insulin/insulin-like growth factor 1 (IGF1)-like signalling (IIS) pathway of Haemonchus contortus inferred from full-length transcripts and their Caenorhabditis elegans homologs. (PDF 216 kb

Additional file 1: of Pipeline for the identification and classification of ion channels in parasitic flatworms

Table S1. Sequence counts per ion channel family obtained from the KEGG and SwissProt databases and included in the training and test datasets. Table S2. Accession numbers of ion channels selected for support vector machine model training. Table S3. The number of sequences in the testing dataset before and after BLASTp analyses. Table S4. The number of identified test data sequences from humans and C. elegans within each group and divided into known ion channel and non-ion channel datasets. Table S5. Cross-validation, training and testing accuracies of each model. Table S6. Final tables of confusion matrices for the “Classifier” and “Dipeptide” models. Table S7. Summary of flatworm ion channels predicted using the MuSICC identification and classification pipeline with high and medium confidence. Table S8. Complete set of flatworm ion channels predicted using the MuSICC identification and classification pipeline. (XLS 2960 kb

Additional file 1: Table S1. of MicroRNAs of Toxocara canis and their predicted functional roles

Primers for microRNA quantitative PCR. Table S2. Transcription profiles of known and novel microRNAs in Toxocara canis. Table S3. Gene ontology (GO) annotation and pathway enrichment analyses of microRNAs of Toxocara canis. Table S4. MicroRNAs differentially transcribed between male and female adults of Toxocara canis. Table S5. Sequence-dependent transcription between male and female adults of Toxocara canis. Table S6. GO annotation and pathway enrichment analysis of microRNAs differentially transcribed between male and female adults of Toxocara canis. Table S7. MicroRNA seed sequence families predicted to be involved in reproduction and larval development. Table S8. MicroRNAs predicted to be linked to host-parasite interactions. Table S9. MicroRNAs predicted to be drug targets or to have a link to drug resistance. (XLS 948 kb